Large databases are frequently used to store information of all types, such as financial, customer, inventory, etc. However, databases are becoming more complex due to, for example, increasing number of users, fields in the tables, and relations between various tables and their items. Access to the tables is also limited by various factors, such as disk speed and synchronization of calls to a database table. The increasing size of the tables and the relations in the corresponding data only decreases the speed and accessibility of information from a database table. Computer programs, such as financial or banking programs, frequently must access databases with millions of processes and objects. If objects are processed linearly, the time taken to complete any given task would increase (at least) proportionally to the number of objects needed to be processed. This is unacceptable with the growing complexity of processes and calls to the database. However, processing objects in parallel to access a database also presents a synchronization problem. Two objects may have a relation between each other (e.g., in an example banking context, a loan and its collateral) and have to be processed together (e.g. to calculate the credit risk for the loan mitigated by its collateral). These relations can be arbitrarily complex, they are not restricted e.g. to trees with a clearly identified root. If two objects are processed in parallel at the same time this may lead to duplicate results. The synchronization needed to avoid the conflicts often leads to a decrease in performance that can at worst be slower than if the process was carried out sequentially. This synchronization is a particular problem if the parallel processes are running on different machines that do not have a fast communication path (such as a shared memory) but only a network connection. A method and system is needed to be able to process objects in parallel to decrease the time needed, while at the same time avoiding duplicate results. The efficiency gain using a separate bundling algorithm is especially significant if the second step (“calculation process”) is very processing intensive.